用大模型同时扩展查询广度和深度，提升旅行推荐准确性
Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation
- 结合广度与深度，生成信息丰富的子主题查询
- 在新数据集上召回率和精确率显著优于现有方法
- 适合需要理解模糊或宽泛旅行需求的推荐系统
在查询驱动的旅行推荐系统中，理解如“青年友好活动”或“高中毕业旅行”这类宽泛、间接的自然语言查询至关重要。这类查询因用户意图范围广且微妙，导致检索方法难以从文本描述（如WikiVoyage）中准确推断相关目的地。尽管查询重写（QR）已被证明可提升检索效果，但现有方法通常只关注扩大潜在匹配子主题范围（广度）或深化查询语义（深度），而无法兼顾二者。本文提出一种基于大语言模型的细粒度子主题查询重写方法EQR，通过生成兼具广度与深度的信息丰富型子主题，同时提升覆盖范围与语义精准度。我们还发布了TravelDest——一个专用于查询驱动旅行推荐的新数据集。在该数据集上的实验表明，EQR相比现有最先进方法，在召回率和精确率上均有显著提升。
原文摘要 · Abstract (English)
In Query-driven Travel Recommender Systems (RSs), it is crucial to understand the user intent behind challenging natural language(NL) destination queries such as the broadly worded "youth-friendly activities" or the indirect description "a high school graduation trip". Such queries are challenging due to the wide scope and subtlety of potential user intents that confound the ability of retrieval methods to infer relevant destinations from available textual descriptions such as WikiVoyage. While query reformulation (QR) has proven effective in enhancing retrieval by addressing user intent, existing QR methods tend to focus only on expanding the range of potentially matching query subtopics (breadth) or elaborating on the potential meaning of a query (depth), but not both. In this paper, we introduce Elaborative Subtopic Query Reformulation (EQR), a large language model-based QR method that combines both breadth and depth by generating potential query subtopics with information-rich elaborations. We also release TravelDest, a novel dataset for query-driven travel destination RSs. Experiments on TravelDest show that EQR achieves significant improvements in recall and precision over existing state-of-the-art QR methods.
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